major update to dto results across most of the binding/perturbation datasets

.gitignore

@@ -0,0 +1 @@
+deprecated/

dto/{binding_repo_dataset=harbison_2004-harbison_2004/perturbation_repo_dataset=Hackett_2020-hackett_2020 → binding_repo_dataset=callingcards-annotated_features/perturbation_repo_dataset=hackett_2020-hackett_2020}/part-0.parquet

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dto/binding_repo_dataset=callingcards-annotated_features/perturbation_repo_dataset=hu_2007_reimand_2010-hu_2007_reimand_2010/part-0.parquet

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dto/binding_repo_dataset=callingcards-annotated_features/{perturbation_repo_dataset=Hackett_2020-hackett_2020 → perturbation_repo_dataset=hughes_2006-knockout}/part-0.parquet

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scripts/dto_preparation.R

@@ -88,100 +88,26 @@ single_cc = arrow::open_dataset("~/code/hf/callingcards/annotated_features") %>%
     left_join(single_cc_meta) %>%
     mutate(id = as.character(id))
 
+# note: filter these for the mahendrawada features, too. Restricts analysis
+# to only non dubious genomic loci
 binding_data = list(
     cc = single_cc %>%
         select(intersect(colnames(.), colnames(composite_cc))) %>%
         bind_rows(composite_cc %>%
-                      select(intersect(colnames(.), colnames(single_cc)))),
+                      select(intersect(colnames(.), colnames(single_cc)))) %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag),
     harbison = arrow::read_parquet("~/code/hf/harbison_2004/harbison_2004.parquet") %>%
         replace_na(list(effect = 0, pvalue = 1)) %>%
         group_by(sample_id, target_locus_tag) %>%
         slice_max(abs(effect), n = 1, with_ties = FALSE) %>%
-        ungroup(),
+        ungroup() %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag),
     chipexo = arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_af_combined.parquet") %>%
-        left_join(arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata_sample.parquet")),
+        left_join(arrow::read_parquet("~/code/hf/rossi_2021/rossi_2021_metadata_sample.parquet")) %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag),
     mahendrawada_chec = arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined.parquet") %>%
-        left_join(arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_meta.parquet"))
-)
-
-mahendrawada_rnaseq_dto_background = map(binding_data, ~{
-    .x %>%
-        ungroup() %>%
-        select(target_locus_tag) %>%
-        distinct() %>%
-        filter(target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag))
-    })
-
-mahendrawada_rnaseq_dto = list(
-    cc = list(
-        binding = binding_data$cc %>%
-            filter(poisson_pval <= 0.1) %>%
-            filter(regulator_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$regulator_locus_tag),
-                   target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag)) %>%
-            group_by(id) %>%
-            arrange(desc(callingcards_enrichment)) %>%
-            mutate(pvalue_rank = rank(poisson_pval, ties.method = 'min')) %>%
-            dplyr::rename(sample_id = id) %>%
-            group_by(sample_id),
-        pr = perturbation_response_data$mahendrawada_rnaseq %>%
-            filter(pvalue <= 0.1) %>%
-            filter(regulator_locus_tag %in% unique(binding_data$cc$regulator_locus_tag),
-                   target_locus_tag %in% unique(binding_data$cc$target_locus_tag)) %>%
-            group_by(sample_id) %>%
-            mutate(abs_log2fc_rank = rank(-abs_log2fc, ties.method = 'min'),
-                   pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-            group_by(sample_id)),
-    harbison = list(
-        binding = binding_data$harbison %>%
-            filter(pvalue <= 0.1) %>%
-            filter(regulator_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$regulator_locus_tag),
-                   target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag)) %>%
-            group_by(sample_id) %>%
-            arrange(desc(effect)) %>%
-            mutate(pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-            group_by(sample_id),
-        pr = perturbation_response_data$mahendrawada_rnaseq %>%
-            filter(pvalue <= 0.1) %>%
-            filter(regulator_locus_tag %in% unique(binding_data$harbison$regulator_locus_tag),
-                   target_locus_tag %in% unique(binding_data$harbison$target_locus_tag)) %>%
-            group_by(sample_id) %>%
-            mutate(abs_log2fc_rank = rank(-abs_log2fc, ties.method = 'min'),
-                   pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-            group_by(sample_id)),
-    chipexo = list(
-        binding = binding_data$chipexo %>%
-            filter(log_poisson_pval <= log(0.1)) %>%
-            filter(regulator_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$regulator_locus_tag),
-                   target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag)) %>%
-            group_by(sample_id) %>%
-            arrange(desc(enrichment)) %>%
-            mutate(pvalue_rank = rank(log_poisson_pval, ties.method = 'min')) %>%
-            group_by(sample_id),
-        pr = perturbation_response_data$mahendrawada_rnaseq %>%
-            filter(pvalue <= 0.1) %>%
-            filter(regulator_locus_tag %in% unique(binding_data$chipexo$regulator_locus_tag),
-                   target_locus_tag %in% unique(binding_data$chipexo$target_locus_tag)) %>%
-            group_by(sample_id) %>%
-            mutate(abs_log2fc_rank = rank(-abs_log2fc, ties.method = 'min'),
-                   pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-            group_by(sample_id)),
-    mahendrawada_chec = list(
-        binding = binding_data$mahendrawada_chec %>%
-            filter(log_poisson_pval <= log(0.1)) %>%
-            filter(regulator_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$regulator_locus_tag),
-                   target_locus_tag %in% unique(perturbation_response_data$mahendrawada_rnaseq$target_locus_tag)) %>%
-            group_by(sample_id) %>%
-            arrange(desc(enrichment)) %>%
-            mutate(pvalue_rank = rank(log_poisson_pval, ties.method = 'min')) %>%
-            group_by(sample_id),
-        pr = perturbation_response_data$mahendrawada_rnaseq %>%
-            filter(pvalue <= 0.1) %>%
-            filter(regulator_locus_tag %in% unique(binding_data$mahendrawada_chec$regulator_locus_tag),
-                   target_locus_tag %in% unique(binding_data$mahendrawada_chec$target_locus_tag)) %>%
-            group_by(sample_id) %>%
-            mutate(abs_log2fc_rank = rank(-abs_log2fc, ties.method = 'min'),
-                   pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-            group_by(sample_id))
+        left_join(arrow::read_parquet("~/code/hf/mahendrawada_2025/chec_mahendrawada_m2025_af_combined_meta.parquet")) %>%
+        filter(target_locus_tag %in% mahendrawada_features$locus_tag)
 )
 
 # Function to create DTO for a given PR dataset
@@ -232,7 +158,12 @@ create_pr_dto = function(pr_data, pr_effect_col, pr_pval_col, binding_data_list)
                 group_by(sample_id) %>%
                 mutate(abs_effect_rank = rank(-abs(effect), ties.method = 'min'),
                        pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-                group_by(sample_id)),
+                group_by(sample_id),
+            background = pr_standardized %>%
+                filter(regulator_locus_tag %in% unique(binding_data_list$cc$regulator_locus_tag),
+                       target_locus_tag %in% unique(binding_data_list$cc$target_locus_tag)) %>%
+                pull(target_locus_tag) %>%
+                unique()),
 
         harbison = list(
             binding = binding_data_list$harbison %>%
@@ -251,7 +182,12 @@ create_pr_dto = function(pr_data, pr_effect_col, pr_pval_col, binding_data_list)
                 group_by(sample_id) %>%
                 mutate(abs_effect_rank = rank(-abs(effect), ties.method = 'min'),
                        pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-                group_by(sample_id)),
+                group_by(sample_id),
+            background = pr_standardized %>%
+                filter(regulator_locus_tag %in% unique(binding_data_list$harbison$regulator_locus_tag),
+                       target_locus_tag %in% unique(binding_data_list$harbison$target_locus_tag)) %>%
+                pull(target_locus_tag) %>%
+                unique()),
 
         chipexo = list(
             binding = binding_data_list$chipexo %>%
@@ -270,7 +206,12 @@ create_pr_dto = function(pr_data, pr_effect_col, pr_pval_col, binding_data_list)
                 group_by(sample_id) %>%
                 mutate(abs_effect_rank = rank(-abs(effect), ties.method = 'min'),
                        pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-                group_by(sample_id)),
+                group_by(sample_id),
+            background = pr_standardized %>%
+                filter(regulator_locus_tag %in% unique(binding_data_list$chipexo$regulator_locus_tag),
+                       target_locus_tag %in% unique(binding_data_list$chipexo$target_locus_tag)) %>%
+                pull(target_locus_tag) %>%
+                unique()),
 
         mahendrawada_chec = list(
             binding = binding_data_list$mahendrawada_chec %>%
@@ -289,7 +230,12 @@ create_pr_dto = function(pr_data, pr_effect_col, pr_pval_col, binding_data_list)
                 group_by(sample_id) %>%
                 mutate(abs_effect_rank = rank(-abs(effect), ties.method = 'min'),
                        pvalue_rank = rank(pvalue, ties.method = 'min')) %>%
-                group_by(sample_id))
+                group_by(sample_id),
+            background = pr_standardized %>%
+                filter(regulator_locus_tag %in% unique(binding_data_list$mahendrawada_chec$regulator_locus_tag),
+                       target_locus_tag %in% unique(binding_data_list$mahendrawada_chec$target_locus_tag)) %>%
+                pull(target_locus_tag) %>%
+                unique())
     )
 
     return(dto_list)
@@ -341,7 +287,7 @@ all_pr_dtos = list(
 )
 
 # Write out DTO ranked lists
-
+# Write out DTO ranked lists
 write_out_pr_dto_lists = function(pr_dataset_name,
                                   binding_pr_set_name,
                                   all_pr_dtos_list,
@@ -393,16 +339,9 @@ write_out_pr_dto_lists = function(pr_dataset_name,
             write_csv(file.path(curr_output_path$pr_pvalue, paste0(.x, ".csv")),
                       col_names = FALSE)
     })
-}
 
-# Generalized function to write background
-write_pr_background = function(pr_dataset_name,
-                               binding_pr_set_name,
-                               background_list,
-                               base_outdir = here("results/dto")) {
-    output_path = file.path(base_outdir, pr_dataset_name)
-
-    background_list[[binding_pr_set_name]] %>%
+    # Write out background
+    tibble(target_locus_tag = binding_pr_set$background) %>%
         write_csv(file.path(output_path, binding_pr_set_name, "background.csv"),
                   col_names = FALSE)
 }
@@ -464,44 +403,32 @@ create_pr_lookups = function(pr_dataset_name, binding_pr_set_name,
         incomplete_after_filtering = incomplete_after_filtering
     ))
 }
-# Create background lists for all PR datasets (if not already created)
-all_pr_backgrounds = map(names(all_pr_dtos), ~{
-    map(binding_data, function(bd) {
-        bd %>%
-            ungroup() %>%
-            select(target_locus_tag) %>%
-            distinct() %>%
-            filter(target_locus_tag %in% unique(all_pr_dtos[[.x]][[1]]$pr$target_locus_tag))
-    })
-})
-names(all_pr_backgrounds) = names(all_pr_dtos)
-
-# Write out all DTOs for all PR datasets
-lookup_results = list()
 
-dto_input_outdir = here("results/dto")
-for (pr_name in names(all_pr_dtos)) {
-    lookup_results[[pr_name]] = list()
-
-    for (binding_name in names(all_pr_dtos[[pr_name]])) {
-        write_out_pr_dto_lists(pr_name, binding_name, all_pr_dtos)
-        write_pr_background(pr_name, binding_name, all_pr_backgrounds[[pr_name]])
-
-        lookup_result = create_pr_lookups(pr_name, binding_name, all_pr_dtos)
-        lookup_results[[pr_name]][[binding_name]] = lookup_result
-
-        # Write complete lookups only
-        lookup_result$lookup %>%
-            write_tsv(file.path(dto_input_outdir, pr_name, binding_name, "lookup.txt"),
-                      col_names = FALSE)
-
-        # Write incomplete cases for reference
-        if (nrow(lookup_result$incomplete_after_filtering) > 0) {
-            lookup_result$incomplete_after_filtering %>%
-                write_csv(file.path(dto_input_outdir, pr_name, binding_name, "incomplete.csv"))
-        }
-    }
-}
+# # Write out all DTOs for all PR datasets
+# lookup_results = list()
+#
+# dto_input_outdir = here("results/dto")
+# for (pr_name in names(all_pr_dtos)) {
+#     lookup_results[[pr_name]] = list()
+#
+#     for (binding_name in names(all_pr_dtos[[pr_name]])) {
+#         write_out_pr_dto_lists(pr_name, binding_name, all_pr_dtos)
+#
+#         lookup_result = create_pr_lookups(pr_name, binding_name, all_pr_dtos)
+#         lookup_results[[pr_name]][[binding_name]] = lookup_result
+#
+#         # Write complete lookups only
+#         lookup_result$lookup %>%
+#             write_tsv(file.path(dto_input_outdir, pr_name, binding_name, "lookup.txt"),
+#                       col_names = FALSE)
+#
+#         # Write incomplete cases for reference
+#         if (nrow(lookup_result$incomplete_after_filtering) > 0) {
+#             lookup_result$incomplete_after_filtering %>%
+#                 write_csv(file.path(dto_input_outdir, pr_name, binding_name, "incomplete.csv"))
+#         }
+#     }
+# }
 
 # Summary of incomplete cases across all datasets
 incomplete_summary = map_dfr(names(lookup_results), ~{
@@ -512,84 +439,3 @@ incomplete_summary = map_dfr(names(lookup_results), ~{
 })
 
 print(incomplete_summary %>% count(pr_dataset, binding_dataset, missing_type))
-
-dto_results_path_list = list.files(base_outdir,
-                              "*.json",
-                              recursive = TRUE)
-
-## Parse the results -- note that this needs to be adjusted for the
-## expanded set of perturbation response
-
-dto_results_frames_list = map(file.path(base_outdir, dto_results_path_list),
-                              ~as_tibble(jsonlite::read_json(.x)))
-names(dto_results_frames_list) = dto_results_path_list
-dto_results_frame = bind_rows(dto_results_frames_list, .id = 'path')
-
-curr_dto_res = arrow::open_dataset("~/code/hf/yeast_comparative_analysis/dto") %>%
-    collect()
-
-results_df = tibble(
-    combined_id = str_remove(basename(dto_results_list), ".json"),
-    binding_source = dirname(dirname(dirname(dirname(dto_results_list)))),
-    pr_scoring = basename(dirname(dto_results_list)),
-    path = dto_results_path_list) %>%
-    separate_wider_delim(combined_id,
-                         names = c('binding_sampleid', 'pr_sampleid'),
-                         delim = '-_-') %>%
-    left_join(dto_results_frame) %>%
-    select(-path) %>%
-    mutate(
-        binding_id = case_when(
-            binding_source == "cc" & str_detect(binding_sampleid, "-")
-            ~ paste0("BrentLab/callingcards;annotated_features_combined",
-                     binding_sampleid),
-            binding_source == "cc" & str_detect(binding_sampleid, "-", negate=TRUE)
-            ~ paste0("BrentLab/callingcards;annotated_features",
-                     binding_sampleid),
-            binding_source == "chipexo"
-            ~ paste0("BrentLab/rossi_2021;rossi_2021_af_combined",
-                     binding_sampleid),
-            binding_source == "harbison"
-            ~ paste0("BrentLab/harbison_2004;harbison_2004",
-                     binding_sampleid),
-            binding_source == "mahendrawada_chec"
-            ~ paste0("BrentLab/mahendrawada_2025;chec_mahendrawada_m2025_af_combined",
-                     binding_sampleid)),
-        perturbation_id = paste0("BrentLab/mahendrawada_2025/rnaseq_reprocessed", pr_sampleid),
-        binding_repo_dataset = case_when(
-            binding_source == "cc" & str_detect(binding_sampleid, "-")
-            ~ "callingcards-annotated_features_combined",
-            binding_source == "cc" & str_detect(binding_sampleid, "-", negate=TRUE)
-            ~ "callingcards-annotated_features",
-            binding_source == "chipexo"
-            ~ "rossi_2021-rossi_2021_af_combined",
-            binding_source == "harbison"
-            ~ paste0("harbison_2004-harbison_2004"),
-            binding_source == "mahendrawada_chec"
-            ~ "mahendrawada_2025-chec_mahendrawada_m2025_af_combined"),
-        perturbation_repo_dataset = "mahendrawada_2025-rnaseq_reprocessed") %>%
-    dplyr::rename(binding_rank_threshold = rank1,
-                  perturbation_rank_threshold = rank2,
-                  binding_set_size = set1_len,
-                  perturbation_set_size = set2_len,
-                  dto_fdr = fdr,
-                  dto_empirical_pvalue = empirical_pvalue) %>%
-    select(binding_id, perturbation_id,
-           binding_rank_threshold, perturbation_rank_threshold,
-           binding_set_size, perturbation_set_size,
-           dto_fdr, dto_empirical_pvalue,
-           binding_repo_dataset, perturbation_repo_dataset)
-
-# arrow::write_dataset(
-#     results_df,
-#     path = "/home/chase/code/hf/yeast_comparative_analysis/dto",
-#     format = "parquet",
-#     partitioning = c("binding_repo_dataset", "perturbation_repo_dataset"),
-#     existing_data_behavior = "overwrite",
-#     compression = "zstd",
-#     write_statistics = TRUE,
-#     use_dictionary = c(
-#         binding_id = TRUE,
-#         perturbation_id = TRUE
-#     )
-# )

scripts/parse_dto_results.R

@@ -0,0 +1,147 @@
+library(tidyverse)
+library(furrr)
+library(here)
+
+dto_results_lookup = expand_grid(
+    pr_source = c("hackett", "hughes_oe", "hughes_ko", "hu_reimand", "kemmeren", "mahendrawada_rnaseq"),
+    binding_source = c("cc", "chipexo", "harbison", "mahendrawada_chec"),
+    pr_ranking_column = c("log2fc", "pvalue")) %>%
+    mutate(
+        result_file = map(file.path(here("results/dto"),
+                                    pr_source,
+                                    binding_source, "results",
+                                    pr_ranking_column),
+                          ~list.files(.x, full.names = TRUE))) %>%
+    unnest(result_file) %>%
+    mutate(tmp = str_remove(basename(result_file), ".json")) %>%
+    separate_wider_delim(tmp,
+                         names = c('binding_id', 'perturbation_id'),
+                         delim = "-_-")
+
+plan(multisession, workers = 20)
+
+# Function with retry logic
+read_json_with_retry = function(filepath, max_retries = 1) {
+    for (attempt in 1:max_retries) {
+        result = tryCatch({
+            pivot_wider(enframe(as_vector(jsonlite::read_json(filepath))))
+        }, error = function(e) {
+            if (attempt < max_retries) {
+                message(sprintf("Attempt %d/%d failed for %s: %s. Retrying...",
+                                attempt, max_retries, filepath, e$message))
+                Sys.sleep(2^(attempt - 1))  # Exponential backoff: 1s, 2s, 4s, 8s, 16s
+                NULL
+            } else {
+                stop(sprintf("Failed after %d attempts for file: %s\nError: %s",
+                             max_retries, filepath, e$message))
+            }
+        })
+
+        if (!is.null(result)) {
+            return(result)
+        }
+    }
+}
+
+dto_results_list = future_imap(dto_results_lookup$result_file, ~{
+    tryCatch({
+        # Check if file is empty
+        if (file.size(.x) == 0) {
+            return(tibble(
+                empirical_pvalue = NA_real_,
+                fdr = NA_real_,
+                population_size = NA_real_,
+                rank1 = NA_real_,
+                rank2 = NA_real_,
+                set1_len = NA_real_,
+                set2_len = NA_real_,
+                unpermuted_intersection_size = NA_real_,
+                unpermuted_pvalue = NA_real_
+            ))
+        }
+        read_json_with_retry(.x)
+    }, error = function(e) {
+        stop(sprintf("Index %d failed:\nFile: %s\nError: %s",
+                     .y, .x, e$message))
+    })
+}, .progress = TRUE, .options = furrr_options(seed = TRUE))
+
+dto_results_df = bind_cols(dto_results_lookup, bind_rows(dto_results_list))
+
+hf_results_df = dto_results_df %>%
+    mutate(
+        binding_id = case_when(
+            binding_source == "cc" & str_detect(binding_id, "-")
+            ~ paste0("BrentLab/callingcards;annotated_features_combined;",
+                     binding_id),
+            binding_source == "cc" & str_detect(binding_id, "-", negate=TRUE)
+            ~ paste0("BrentLab/callingcards;annotated_features;",
+                     binding_id),
+            binding_source == "chipexo"
+            ~ paste0("BrentLab/rossi_2021;rossi_2021_af_combined;",
+                     binding_id),
+            binding_source == "harbison"
+            ~ paste0("BrentLab/harbison_2004;harbison_2004;",
+                     binding_id),
+            binding_source == "mahendrawada_chec"
+            ~ paste0("BrentLab/mahendrawada_2025;chec_mahendrawada_m2025_af_combined;",
+                     binding_id)),
+        perturbation_id = case_when(
+            pr_source == "hackett" ~ paste0("BrentLab/hackett_2020;hackett_2020;", perturbation_id),
+            pr_source == "hughes_oe" ~ paste0("BrentLab/hughes_2006;overexpression;", perturbation_id),
+            pr_source == "hughes_ko" ~ paste0("BrentLab/hughes_2006;knockout;", perturbation_id),
+            pr_source == "hu_reimand" ~ paste0("BrentLab/hu_2007_reimand_2010;hu_2007_reimand_2010;", perturbation_id),
+            pr_source == "kemmeren" ~ paste0("BrentLab/kemmeren_2014;kemmeren_2014;", perturbation_id),
+            pr_source == "mahendrawada_rnaseq" ~ paste0("BrentLab/mahendrawada_2025;rnaseq_reprocessed;", perturbation_id),
+            .default = "ERROR"),
+        binding_repo_dataset = case_when(
+            binding_source == "cc" & str_detect(binding_id, "-")
+            ~ "callingcards-annotated_features_combined",
+            binding_source == "cc" & str_detect(binding_id, "-", negate=TRUE)
+            ~ "callingcards-annotated_features",
+            binding_source == "chipexo"
+            ~ "rossi_2021-rossi_2021_af_combined",
+            binding_source == "harbison"
+            ~ paste0("harbison_2004-harbison_2004"),
+            binding_source == "mahendrawada_chec"
+            ~ "mahendrawada_2025-chec_mahendrawada_m2025_af_combined"),
+        perturbation_repo_dataset = case_when(
+            pr_source == "hackett" ~ paste0("hackett_2020-hackett_2020"),
+            pr_source == "hughes_oe" ~ paste0("hughes_2006-overexpression"),
+            pr_source == "hughes_ko" ~ paste0("hughes_2006-knockout"),
+            pr_source == "hu_reimand" ~ paste0("hu_2007_reimand_2010-hu_2007_reimand_2010"),
+            pr_source == "kemmeren" ~ paste0("kemmeren_2014-kemmeren_2014"),
+            pr_source == "mahendrawada_rnaseq" ~ paste0("mahendrawada_2025-rnaseq_reprocessed"),
+            .default = "ERROR")) %>%
+    dplyr::rename(binding_rank_threshold = rank1,
+                  perturbation_rank_threshold = rank2,
+                  binding_set_size = set1_len,
+                  perturbation_set_size = set2_len,
+                  dto_fdr = fdr,
+                  dto_empirical_pvalue = empirical_pvalue) %>%
+    select(binding_id, perturbation_id,
+           binding_rank_threshold, perturbation_rank_threshold,
+           binding_set_size, perturbation_set_size,
+           dto_fdr, dto_empirical_pvalue,
+           binding_repo_dataset, perturbation_repo_dataset)
+
+
+# note that the deprecated directory is ignored in yeast_comparative_analysis.
+# this exists on my computer and could exist on yours, too, if you wanted to
+# put old results in a subdir called deprecated that would be ignored by huggingface/git
+# old_db_dto_res = arrow::open_dataset("~/code/hf/yeast_comparative_analysis/deprecated/dto") %>%
+#     collect()
+
+# arrow::write_dataset(
+#     hf_results_df,
+#     path = "/home/chase/code/hf/yeast_comparative_analysis/dto",
+#     format = "parquet",
+#     partitioning = c("binding_repo_dataset", "perturbation_repo_dataset"),
+#     existing_data_behavior = "overwrite",
+#     compression = "zstd",
+#     write_statistics = TRUE,
+#     use_dictionary = c(
+#         binding_id = TRUE,
+#         perturbation_id = TRUE
+#     )
+# )